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15th International Conference on Knowledge and Smart Technology, KST 2023

作者： Sukumthammarat, Nessara Nimma, Sasima Songmuang, Pokpong Thammasat University College of Interdisciplinary Studies Data Science and Innovation Pathum Thani Thailand Thammasat University Faculty of Science and Technology Department of Computer Science Pathum Thani Thailand

ISBN: (纸本)9781665477123

Twitter is a social media platform where users can post, make a conversation, comments, and share experiences that express their emotions and sentiments. Our objective is to monitor and analyze the #Dek65 hashtag's Twitter messages. We take 166,110 Twitter messages on the #Dek65 hashtag from August 2021 to July 2022 and bring them to analyze attitudes, thoughts, emotions, and stress during the preparation for university entrance exams and the Thai education system. We designed and developed a system by creating a model that can sentiment message, a model for cluster topics from the negative message then represent sentiment messages in a way that is simple to understand through visualization. We do this to make stakeholders can monitor and aware of the problems in the Thai education system. © 2023 ieee.

关键词： Social networking (online)

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Revolutionizing House Price Prediction: Harnessing the Power of data science and Augmented Reality 3

Revolutionizing House Price Prediction: Harnessing the Power...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Biresh Kumari, Manisha Banerjee, Pallab Jha, Pooja Amity Institute of Information Technology Amity University Jharkhand Ranchi India Amity School of Engineering & Technology Amity University Jharkhand Ranchi India

ISBN: (纸本)9798350319125

data science and augmented reality (AR) technology convergence in recent years has cleared the door for creative applications in many different fields. To revolutionize the process of predicting property prices, this study investigates the merging of data science approaches with AR interfaces. This research proposes an innovative strategy to empower homebuyers and real estate agents by utilizing machine learning algorithms and real-time data visualization through AR. The study is concerned with gathering and analyzing huge datasets about property characteristics, neighborhood characteristics, and market movements. To determine the important variables influencing house prices, sophisticated data analysis approaches are used. Users can experience real estate in an immersive, engaging way by using these insights, which are neatly integrated into an AR application. Users of the AR interface can view prices, property details, and comparative studies in real-time as forecasted housing data is superimposed over it. Prediction accuracy is ensured by including user preferences and dynamic updates, which improve personalization. Through user feedback loops, the system's efficiency is verified, allowing the prediction models to be continuously improved. The outcomes show the potential for data science and augmented reality to work together to change the home buying, real estate agent, and development processes. This study not only advances housing market analysis but also establishes a precedent for future data-driven real estate decisions, which will ultimately change how homes are bought and sold. © 2023 ieee.

关键词： EDA (Exploratory data Analysis) Hyperparameter Tuning L1 Plenty L2 Plenty RMSE (Root Mean Squared Error)

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A Clustering Method Unifying Cell-Type Recognition and Subtype Identification for Tumor Heterogeneity Analysis

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ieee-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第2期20卷 822-832页

作者： Li, Xiong Lin, Yangkai Xie, Chengwang Li, Zejun Chen, Min Wang, Peng Zhou, Juan East China Jiaotong Univ Sch Software Nanchang 330013 Peoples R China South China Normal Univ Sch Data Sci & Engn Guangzhou 510000 Peoples R China Hunan Inst Technol Hengyang 421010 Peoples R China Hunan Inst Technol Sch Comp Sci & Technol Hengyang 421010 Peoples R China

The rapid development of single-cell technology has opened up a whole new perspective for identifying cell types in multicellular organisms and understanding the relationships between them. Distinguishing different cell types and subtypes can identify the components of different immune cells and different tumor clones in the tumor microenvironment, which is the basic work of tumor heterogeneity analysis and can help researchers understand the mechanism of tumor immune escape. Existing algorithms treat both cell types and subtypes as populations of cells with specific gene expression patterns, which is not conducive to accurate cell typing. For that, we proposed a cell similarity metric that unifies cell type recognition and subtype identification (UCRSI), with the assumption that selectively expressed genes represent differences in underlying cell type with on/off manner, while differences in expression level represent different cell subtype with more/less manner. Our method calculates these two kinds of differences separately, and then combines them using a consensus adjacency matrix, and finally cell typing is completed using spectral clustering algorithm. The results show that UCRSI can reconstruct expert annotation of single-cell RNA sequencing datasets more robustly than existing methods. And, UCRSI is useful for analyzing tumor heterogeneity and improving visualization of large-scale cell clustering.

关键词： Cell type assignment subtype identification tumor heterogeneity spectral clustering algorithm

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Effects of Motion-Relevant Knowledge From Unlabeled Video to Human-Object Interaction Detection

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5760-5773页

作者： Lin, Xue Zou, Qi Xu, Xixia Huang, Yaping Ding, Ding Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China

The existing works on human-object interaction (HOI) detection usually rely on expensive large-scale labeled image datasets. However, in real scenes, labeled data may be insufficient, and some rare HOI categories have few samples. This poses great challenges for deep-learning-based HOI detection models. Existing works tackle it by introducing compositional learning or word embedding but still need large-scale labeled data or extremely rely on the well-learned knowledge. In contrast, the freely available unlabeled videos contain rich motion-relevant information that can help infer rare HOIs. In this article, we creatively propose a multitask learning (MTL) perspective to assist in HOI detection with the aid of motion-relevant knowledge learning on unlabeled videos. Specifically, we design the appearance reconstruction loss (ARL) and sequential motion mining module in a self-supervised manner to learn more generalizable motion representations for promoting the detection of rare HOIs. Moreover, to better transfer motion-related knowledge from unlabeled videos to HOI images, a domain discriminator is introduced to decrease the domain gap between two domains. Extensive experiments on the HICO-DET dataset with rare categories and the V-COCO dataset with minimum supervision demonstrate the effectiveness of motion-aware knowledge implied in unlabeled videos for HOI detection.

关键词： Task analysis Training visualization Feature extraction Semantics Dynamics Streaming media Domain adaptation human-object interaction (HOI) multitask learning (MTL) self-supervised learning (SSL)

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A Web-Based Learning Platform for Teaching data science to Non-Computer Majors 53

A Web-Based Learning Platform for Teaching Data Science to N...

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53rd ieee ASEE Frontiers in Education International Conference, FIE 2023

作者： Liu, Xumin Golen, Erik Raj, Rajendra Fluet, Kimberly Rochester Institute of Technology Department of Computer Science RochesterNY United States School of Information Rochester Institute of Technology RochesterNY United States University of Rochester Center for Professional Development and Education Reform RochesterNY United States

ISBN: (纸本)9798350336429

A web-based learning platform is useful as it allows students with limited or no programming background to conduct in-depth hands-on practice in data science. Background: The need for data science coursework for non-computing majors has grown in recent years, given the demand in various disciplines. However, a substantial number of current data science courses are inappropriate for non-computing majors as they typically require a long chain of prerequisite courses in computer science and mathematics. Moreover, courses designed for computing majors do not match the preparation and interests of students majoring in other disciplines. Outcomes: This paper presents a platform for Learning data science (DSLP), a web-based platform, which assists in the teaching and learning of data science topics by students with limited or no coding experience, including those that have completed a high school AP Computer science Principles (CSP) class or an equivalent CSP course increasingly offered in many colleges. Application Design: The platform helps students understand fundamental data science concepts and techniques, as well as provides them with an in-depth hands-on experience that goes beyond their coding capabilities. The platform offers various data visualization supports to help students understand data and analysis results. Students can use the platform to work on in-house datasets or their own data. This allows students to focus more on how to solve data science problems in various domains than how to write code. The platform also has several unique features that make it particularly helpful for teaching and learning data science topics such as code exemplification and sandbox, informative instructions, and progress monitoring. Findings: The platform has been used multiple times in data science courses for non-computing majors offered at the authors' institution. Preliminary student feedback indicated that the platform is effective in terms of improving student understand

关键词： component formatting insert style styling

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State Perception and Prediction of Digital Twin Based on Proxy Model

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ieee ACCESS 2023年 11卷 36064-36072页

作者： Wang, Lijun Wang, Chengguang Li, Xiangyang Song, Xiaona Xu, Donglai North China Univ Water Resources & Elect Power Sch Mech Engn Zhengzhou 450045 Peoples R China North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450045 Peoples R China Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough TS1 3BA England

The maintenance of critical components plays a crucial role in ensuring the overall stable operation of equipment and minimizing damages caused by functional errors. However, Traditional operation and maintenance (O&M) modes suffer from problems such as reliance on empirical judgment, lack of data support, insufficient preventive maintenance, and inadequate collaborative management. To address these issues, a viable approach is to adopt more intelligent O&M modes. Based on the characteristics of digital twin technology, such as virtual interaction and real-time feedback, a digital twin framework for critical component maintenance of equipment is proposed, providing a new approach for the practical application of digital twin in intelligent maintenance processes. This framework consists of two key components: the digital twin maintenance model and the proxy model. The process of establishing the digital twin model is elaborated in detail, and a mechanism that integrates digital twin technology and the proxy model is proposed, along with a prediction process based on the fusion of simulation and monitoring data. Finally, based on the summary of the modeling process and the proxy model, a visualization interface for intelligent maintenance of components is built using relevant engineering software.

关键词： Digital twins data models Predictive models Maintenance engineering Analytical models Real-time systems Digital twin operation and maintenance proxy model

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Two ResNet Mini Architectures for Aircraft Wake Vortex Identification

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ieee ACCESS 2023年 11卷 20515-20523页

作者： Duan, Siyuan Pan, Weijun Leng, Yuanfei Zhang, Xiaolei Sichuan Univ Tianfu Engn Oriented Numer Simulat & Software Inno Chengdu 610207 Peoples R China Civil Aviat Flight Univ China Coll Air Traff Management Guanghan 618307 Peoples R China Shantou Univ Clin Inst 2 Med Coll Dept Med Imaging Shantou 515041 Peoples R China

The identification of aircraft wake vortex is an essential issue in the operation of airspace utilization ratio. In particular, accurately identifying wake vortex in fine classification is helpful to guide separation standards under realistic airport conditions that consist of various complex operation scenarios. To stress this issue and improve the efficiency at the same time, we developed two mini architectures with each network of 10 layers by modifying deep residual neural network (ResNet) and describe the results of a study to evaluate the performances for identifying wake vortex in fine classification. For this purpose, we built the wake vortex dataset measured with pulsed Doppler LiDAR at Chengdu Shuangliu International Airport from Aug 16, 2018, to Oct 10, 2018. To support wake vortex identification in fine classification, the classification indices that consider the background wind speeds, wake vortex evaluation and aircraft types were included in the learning and identification tasks. We compared the performance of the two ResNet mini architectures with other lightweight networks by using wake vortex dataset. The experimental results demonstrate that the developed two ResNet mini architectures contribute to competitive wake identification modeling in terms of accuracy and parameter number.

关键词： Aircraft Laser radar Airports data visualization Atmospheric modeling Deep learning Wind speed Wake vortex identification LiDAR lightweight network ResNet

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Learning Disentangled Representation for Multimodal Cross-Domain Sentiment Analysis

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第10期34卷 7956-7966页

作者： Zhang, Yuhao Zhang, Ying Guo, Wenya Cai, Xiangrui Yuan, Xiaojie Nankai Univ Coll Cyber Sci Tianjin Key Lab Network & Data Secur Technol Tianjin 300350 Peoples R China Nankai Univ Coll Comp Sci Tianjin 300350 Peoples R China

Multimodal cross-domain sentiment analysis aims at transferring domain-invariant sentiment information across datasets to address the insufficiency of labeled data. Existing adaptation methods achieve well performance by remitting the discrepancies in characteristics of multiple modalities. However, the expressive styles of different datasets also contain domain-specific information, which hinders the adaptation performance. In this article, we propose a disentangled sentiment representation adversarial network (DiSRAN) to reduce the domain shift of expressive styles for multimodal cross-domain sentiment analysis. Specifically, we first align the multiple modalities and obtain the joint representation through a cross-modality attention layer. Then, we disentangle sentiment information from the multimodal joint representation that contains domain-specific expressive style by adversarial training. The obtained sentiment representation is domain-invariant, which can better facilitate the sentiment information transfer between different domains. Experimental results on two multimodal cross-domain sentiment analysis tasks demonstrate that the proposed method performs favorably against state-of-the-art approaches.

关键词： Sentiment analysis Task analysis Feature extraction visualization Training Correlation Learning systems Adversarial learning disentangled representation learning domain adaptation multimodal sentiment analysis

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Activated Gradients for Deep Neural Networks

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第4期34卷 2156-2168页

作者： Liu, Mei Chen, Liangming Du, Xiaohao Jin, Long Shang, Mingsheng Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China Univ Chinese Acad Sci Chongqing Sch Chongqing 400714 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point problem. In this article, a novel method by acting the gradient activation function (GAF) on the gradient is proposed to handle these challenges. Intuitively, the GAF enlarges the tiny gradients and restricts the large gradient. Theoretically, this article gives conditions that the GAF needs to meet and, on this basis, proves that the GAF alleviates the problems mentioned above. In addition, this article proves that the convergence rate of SGD with the GAF is faster than that without the GAF under some assumptions. Furthermore, experiments on CIFAR, ImageNet, and PASCAL visual object classes confirm the GAF's effectiveness. The experimental results also demonstrate that the proposed method is able to be adopted in various deep neural networks to improve their performance. The source code is publicly available at https://***/LongJin-lab/Activated-Gradients-for-Deep-Neural-Networks.

关键词： Training Deep learning Neural networks Optimization visualization Newton method Eigenvalues and eigenfunctions Exploding gradient problems gradient activation function (GAF) ill-conditioned problems saddle point problems vanishing gradient problems

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Developing an Integrated Dashboard to Analyze Multimodal data for User Experience Evaluation

Developing an Integrated Dashboard to Analyze Multimodal Dat...

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2023 ieee International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Jo, Hyejeong Lee, Junhyeok Park, Hye Won Kim, Minjae Kim, Yeonwoo Lee, Won Hee Kyung Hee University Department of Software Convergence Korea Republic of Kyung Hee University Department of Artificial Intelligence Korea Republic of

ISBN: (纸本)9798350344318

The evaluation of user experience (UX) is a multifaceted process that encompasses a range of methods such as surveys, emotion recognition, and attention recognition. The effectiveness of UX evaluation depends on the integration of dependable and unbiased techniques derived from diverse user-generated data sources. In this context, we introduce a Multimodal INteractive Dashboard (MIND), a web-based platform tailored to furnish a reliable and user-centric avenue for UX evaluation. MIND incorporates three core functionalities: facial emotion recognition, EEG-based attention recognition, and image generation informed by estimated user's poses and affective states. By seamlessly engaging users, MIND facilitates a quantitative assessment of their experiences. The proposed MIND offers multimodal data analytics and visualization to enable insights of time spans of the UX with individual users, helping improve the overall user experience. © 2023 ieee.

关键词： Emotion Recognition

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